Sleep recordings are important for the analysis, diagnosis, and treatment of various sleep disorders. Sleep staging, in turn, is a vital step in sleep analysis. Sleep staging is normally performed using the traditional Rechtschaffen & Kales (R&K) rules, which classify sleep into six separate stages: wake, rapid eye movement (REM) sleep, and S1 (light sleep) to S4 (deep sleep).
One drawback related to the traditional sleep studies is that the sleep recordings are made in separate sleep research laboratories. Due to the costly equipment involved and the trained personnel needed, the number of the laboratories is low and patients referred to a laboratory may have to travel far away. Furthermore, even though the sleep research laboratories may be comfortably furnished, many patients may find it hard to sleep naturally in these test environments.
The traditional R&K sleep staging also involves several disadvantages. First, the recording may be inconvenient for the patient due to the high number of electrodes and associated leads needed. In the traditional R&K staging, at least four channels are needed for recording an electroencephalogram (EEG), an electromyogram (EMG), and an electro-oculogram (EOG). Second, the R&K rules are insufficient and leave room for subjective interpretation. Due to this, inter-scorer variability is large and it is difficult to automate the staging process reliably. Third, the staging has a rather low temporal resolution, which neglects the micro-structure of sleep.
Various methods for automated sleep classification have been designed which rest on an EEG signal measured from the patient.
U.S. Pat. No. 5,154,180 discloses a method based on the correlations of the successive EEG epochs. In this method, the operator (i.e. the user) sets a threshold that affects the number of resulting classifications.
U.S. Pat. No. 6,272,378 discloses a device based on neural networks. A frontal EEG signal, measured through a three-electrode sensor, is first supplied to a hand-held device allowing the patient to perform the measurements in his or her ordinary environment. The data collected by the hand-held device is then delivered to a separate computer unit in which the data is classified by means of instructed neural networks.
The article Flexer et. al.: A reliable probabilistic sleep stager based on a single EEG signal, Artificial Intelligence in Medicine (2005) 33, 199-207, describes a sleep stager based on Hidden Markov models using one EEG signal. The method detects wakefulness, deep sleep, and REM sleep with an accuracy of about 80%. However, the calculations required are rather complex, which makes the device computationally expensive.
A less complex method for estimating the sleep stages based on an EEG signal is to use the entropy values of the EEG signal. As a study by Burioka et. al. shows, the values of approximate entropy decrease as the sleep gets deeper, cf. Burioka et. al.: Approximate Entropy in the Electroencephalogram During Wake and Sleep, Clinical EEG and Neuroscience, Vol. 36 No. 1, pp. 21-24. However, REM sleep causes problems since the entropy values during REM sleep correspond to those of the awake state and stage 1 non-REM sleep.
The above-described automated methods that rest on a frontal EEG signal are advantageous in the sense that the inconvenience caused to the patient/user may be reduced due to the low number of electrodes and connection wires needed. However, the methods are restricted due to the classification-based approach adopted. As the depth of the sleep is reflected only in the distinct stages determined, the current automated methods are unable to provide a clear description of the continuum of the depth of sleep. This applies especially to transitions from one sleep stage to another, which may not be abrupt changes but may take place more or less gradually.
The present invention seeks to alleviate or eliminate the above-mentioned drawbacks.